Swapping roles with humans can boost robot productivity

Researchers at the Massachusetts Institute of Technology (MIT) have discovered that robots can operate more effectively if their training includes swapping roles with humans working alongside them.

The two researchers – Julie Shah, head of the Interactive Robotics Group in MIT’s Computer Science and Artificial Intelligence Laboratory, and Stefanos Nikolaidis, a PhD student – investigated whether techniques that have been shown to work well when training people, could also be applied to mixed teams of humans and robots. In one such technique, known as cross-training, team members swap roles with each other on given days. “This allows people to form a better idea of how their role affects their partner and how their partner’s role affects them,” Shah explains.

In a paper presented at a recent conference on Human-Robot Interaction in Tokyo, Shah and Nikolaidis gave the results of experiments they carried out with a mixed group of humans and robots, demonstrating that cross-training is an effective team-building tool.

The pair first had to design a algorithm to allow the robots to learn from role-swapping. They modified existing reinforcement-learning algorithms to allow the machines to take in not only information from positive and negative rewards, but also information gained through demonstration. By watching their human counterparts switch roles to carry out their work, the robots were able to learn how the humans wanted them to perform the same task.

Each human-robot team carried out a simulated task in a virtual environment, with half of the teams using a conventional interactive reward (in which a human trainer gives a positive or negative response each time a robot performs a task), and half using the cross-training technique of switching roles halfway through the session. Once the teams had completed this session, they were asked to carry out the task in the real world, this time sticking to their own designated roles.